Cisco Technology, Inc.

Deutschetelekom Ag

Netflix Inc.

Xiaoqing Zhu

Wai-Tian Tan

John George Apostolopoulos

Rong Pan

Rob Liston

Flavio Giovanni Bonomi

Sangeeta Ramakrishnan

Sateesh Kumar Addepalli

Lillian Lei Dai

Preethi Natarajan

Dan Tan

Mehdi Nikkhah

Pascal Thubert

Zhi Li

Joshua B Gahm

Giovanna Carofiglio

Herbert Michael Wildfeuer

Robert Edward Liston

Ashish Khisti

Jerome Henry

Santosh Ghanshyam Pandey

Fabio Rodolfo Maino

Malcolm Muir Smith

Pere Monclus

Vina Ermagan

Vijaynarayanan Subramanian

Hao Hu

Sergio Mena

Alexander Loukissas

Jiantao Fu

Ahmed Atef Younes Badr

Herb Wildfeuer

John T Chapman

Michael Anthony Ramalho

Paul Anthony Polakos

Raghuram S Sudhaakar

Chiu Ngok Eric Wong

John Martin Graybeal

Ashok Krishnaji Moghe

Douglas Chan

Jatinder Pal Singh

Paul Edwin Jones

Te-Yuan Huang

Cheng-Hsin Hsu

Zheng Lu

Kyle Christopher Swanson

Robert Edward Somers

Ahmed Badr

Sergio Mena De La Cruz

Stefano D'Aronco

Marc Jean-Philippe Girardot

Sagar Bharadwaj Kalasibail Seetharam

Shi Su

Wai-tan Tan

Kaidong Wang

Shitanshu V Shah

Nikolaos Markos Freris

Vijaymarayanan Subramanian

Yaqi Wang

Renata Cruz Teixeira

Kaylie Alexa Kwon

In various embodiments, a simulation evaluation application generates a first streaming header based on rungs of a first candidate encoding ladder, where each rung specifies a resolution and a bitrate of a different encoded video. The simulation evaluation application executes an adaptive bitrate algorithm on the first streaming header based on a network throughput trace to determine a first value for a metric that is relevant to quality of experience. The simulation evaluation application generates a second streaming header based on a second candidate encoding ladder. The simulation evaluation application executes the adaptive bitrate algorithm on the second streaming header based on the network throughput trace to determine a second value for the metric. The simulation evaluation application compares the first value to the second value to determine that the first candidate encoding ladder instead of the second candidate encoding ladder should be used to stream the media title.

Simulation-based techniques for evaluating encoding ladders for video streaming

A method for encoding a sequence of packets includes receiving the sequence of packets, generating a parity packet for a first group of packets within the sequence of packets, and transmitting the first group of packets and the parity packet. The parity packet is generated by performing an exclusive OR (XOR) operation over a plurality of packets in the first group of packets and at least one packet in a second group of packets. The second group is separated from the first group in the sequence by one or more packets.

XOR forward error correction for isolated and burst losses over a software-defined-wide area network

Multi-User Multiple Input, Multiple Output (MU-MIMO) data transmissions are provided with a forward-predictive precoding matrix to mitigate the effects of a change in a state of a communication channel. First and second soundings are performed, at first and second times, to a receive antenna over a channel and, responsive to each of the soundings, first and second Channel State Information (CSI) are received. Based on the first and second CSI, a change in a state of the channel over a time period between the first and second time is determined. Based on the change in the state of the channel, a forward-predictive channel state matrix and/or a forward-predictive precoding matrix are determined that reflect a state of the channel at a future time and that are consistent with the determined change in the state over the time period. The forward-predictive precoding matrix is applied to a data transmission.

Forward predictive precoded MIMO

In one embodiment, a service receives machine learning-based generative models from a plurality of distributed sites. Each generative model is trained locally at a site using unlabeled data observed at that site to generate synthetic unlabeled data that mimics the unlabeled data used to train the generative model. The service receives, from each of the distributed sites, a subset of labeled data observed at that site. The service uses the generative models to generate synthetic unlabeled data. The service trains a global machine learning-based model using the received subsets of labeled data received from the distributed sites and the synthetic unlabeled data generated by the generative models.

Communication efficient machine learning of data across multiple sites

A method is provided in a wireless access point in a wireless communications network. The method includes obtaining information characterizing a first wireless stream and the second wireless stream transmitted or received by the wireless access point. The information includes at least a wireless channel quality for each of the first wireless stream and the second wireless stream. The method further includes allocating transmission resources to the first wireless stream and the second wireless stream based on the obtained information. In response to a change in quality of the first wireless stream, the method further includes revising the allocation of transmission resources for the first wireless stream based on at least one of a target bit-rate and a target level of smoothness.

Adaptive resource allocation for media streams over wireless

A controller of a network, including routers to forward flows of packets originated at senders to receivers along distinct network paths each including multiple links, such that the flows merge at a common link that imposes a traffic bottleneck on the flows, receives from one or more of the routers router reports that each indicate an aggregate packet loss that represents an aggregate of packet losses experienced by each of the flows at the common link. The controller sends to the senders aggregate loss reports each including the aggregate packet loss so that the senders have common packet loss information for the common link on which to base decisions as to whether to switch from delay-based to loss-based congestion control modes when implementing dual-mode congestion control of the flows. In lieu of the controller, another example employs in-band router messages populated with packet losses by the routers the messages traverse.

Network-based coordination of loss/delay mode for congestion control of latency-sensitive flows

Techniques for determining a range between a wireless station (STA) and a wireless access point (AP) using channel state information are described. An AP determines channel state information corresponding to an STA. The AP determines, based on the channel state information, one or more fine timing measurement (FTM) parameters. A plurality of FTM messages are transmitted between the AP and the STA, based on the one or more FTM parameters. The STA is configured to determine an estimated range to the AP based on the plurality of FTM messages.

Wireless station ranging using channel state

Embodiments include technologies for creating a manifest for a conferencing event in a network, adding a name tag identifying the conferencing event to the manifest, receiving an interest packet including one or more parameters indicating a named flow being produced at a source node, adding content metadata of the named flow to the manifest, and sending the manifest to the source node. Further embodiments include adding, to the manifest, session-level metadata associated with a user of the source node. Embodiments include receiving a second interest packet with one or more second parameters identifying a user of a client node, where the second interest packet indicates a request to authorize the user of the client node to subscribe to the conferencing event. In further embodiments, session-level metadata associated with the user is added to the manifest if the user is authorized to subscribe to the conferencing event.

Multiparty real-time communications support over information-centric networking

Presented herein are techniques for training a central/global machine learning model in a distributed machine learning system. In the data sampling techniques, a subset of the data obtained at the local sites is intelligently selected for transfer to the central site for use in training the central machine learning model. In the model merging techniques, distributed local training occurs in each local site and copies of the local machine learning models are sent to the central site for aggregation of learning by merging of the models. As a result, in accordance with the examples presented herein, a central machine learning model can be trained based on various representations/transformations of data seen at the local machine learning models, including sampled selections of data-label pairs, intermediate representation of training errors, or synthetic data-label pairs generated by models trained at various local sites.

Training distributed machine learning with selective data transfers

An access point (AP) is configured to transmit packets to a client device over a communication channel. The AP determines a motion indictor indicative of motion of the client device based on a sequence of channel state information measurements, and measures a signal-to-noise ratio (SNR). The AP selects a transmission parameter among candidate transmission parameters using a learning-based algorithm based on observation parameters including the motion indicator, the SNR, and a device identifier for the client device. The AP employs the transmission parameter to transmit packets to the client device, and measures a transmission performance associated with the transmission parameter based on the transmitted packets. The AP updates the learning-based algorithm based on the observation parameters and the transmission performance for a next pass through the selecting, the employing, and the measuring.

Learning-based wireless transmission parameter adaptation based on client activity detection

A method in one embodiment includes intercepting a message in an on-board unit (OBU) of a vehicular network environment between a source and a receiver in the vehicular network environment, verifying the message is sent from the source, verifying the message is not altered, evaluating a set of source flow control policies associated with the source, and blocking the message if the set of source flow control policies indicate the message is not permitted. In specific embodiments, the message is not permitted if a level of access assigned to the source in the set of source flow control policies does not match a level of access tagged on the message. In further embodiments, the method includes evaluating a set of receiver flow control policies associated with the receiver, and blocking the message if the set of receiver flow control policies indicates the message is not permitted.

System and method for wireless interface selection and for communication and access control of subsystems, devices, and data in a vehicular environment

Multi-Class Orthogonal Frequency-Division Multiple Access (OFDMA) scheduling may be provided. A plurality of network devices may be assigned a transmission window for channel access. The plurality of network devices may include a first number of client devices and a second number of client devices. A first signal may be sent to the first number of network devices. The first signal may enable the first number of network devices to the channel access in a first portion of the transmission window. An amount of data to be exchanged by each of the second number of network devices may be determined. Based on the determined amount of data to be exchanged, a schedule may be determined for the channel access for each of the second number of network devices in a second portion of the transmission window. A second signal may be sent to the second number of network devices based on the determined schedule. The second signal may enable the second number of network devices to the channel access in the second portion of the transmission window.

Multi-class orthogonal frequency-division multiple access (OFDMA) scheduling

In one embodiment, a device determines locations of a plurality of transmitters relative to a particular wireless access point in a wireless network. One of the transmitters comprises a target client to which the particular wireless access point is to communicate. The device compares a plurality of beamforming patterns associated with the particular wireless access point to the determined locations. The device selects, based on the comparison, one of the beamforming patterns for use by the particular wireless access point to communicate with the target client. The device controls the particular wireless access point to use the selected beamforming pattern to communicate with the target client.

Wireless beamforming optimization using client location information

Growing community of inventors

Austin, TX, United States of America

Xiaoqing Zhu